Gene Annotation
Gene Annotation - Related Videos
08:09
Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
0 Views •
Cited by 8 •
2012
Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
03:36
Genome Annotation and Assembly
0 Views •
2021
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
06:16
mirMachine: A One-Stop Shop for Plant miRNA Annotation
0 Views •
Cited by 11 •
2021
Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.
08:12
A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers
0 Views •
2025
We present a coding-free workflow for biologists to identify tissue-specific gene enhancers using only browser-based tools. Our protocol leverages public H3K4me1/H3K27ac histone marks and Hi-C data, enabling researchers without programming expertise to access, analyse, and identify potential regulatory elements associated with their genes of interest.
12:55
Using the Gene Pulser MXcell Electroporation System to Transfect Primary Cells with High Efficiency
0 Views •
Cited by 11 •
2010
This procedure shows how to use the Gene Pulser MXcell electroporation system to rapidly and easily identify the best electroporation conditions for mouse embryonic fibroblasts (MEFs) or other primary cells. Considerations for troubleshooting are also discussed in the associated video.
FAQs
Computational prediction identifies likely genomic features from sequence patterns, including coding regions, splice sites, and regulatory elements. Experimental evidence from transcripts or proteins then supports, refines, or challenges those predictions. Combining both sources helps distinguish plausible gene structures from sequence-based possibilities, producing genome maps that are more useful for interpreting gene expression, function, and variation.
Annotation examines several sequence signals rather than relying on a single pattern. Coding regions suggest where protein-producing segments may occur, while splice sites help indicate how those segments may be organized. Regulatory elements provide additional clues about gene control. Considering these features together helps describe gene locations and structures more completely within a DNA sequence.
Transcript and protein data provide evidence connected to biological products, complementing predictions made directly from DNA sequence. Such data can support the presence or organization of a gene and help connect a predicted sequence feature with potential function. This evidence is especially valuable when sequence patterns alone do not fully resolve how a gene should be represented.
Genome annotations are not necessarily fixed records. New transcript or protein evidence, improved sequence interpretation, and recognition of species-specific features can refine gene locations, structures, or proposed functions. Updating databases allows annotations to reflect current knowledge, which improves later studies of expression, evolution, genetic variation, and functional genomics.
A practical workflow begins with the DNA sequence, applies computational searches for coding regions, splice sites, and regulatory elements, and then compares predicted features with available transcript or protein evidence. Similarity to previously characterized genes can add functional clues. The resulting information is organized into a genome map that can support downstream biological analysis.
Annotated genomes support research whenever investigators need to connect DNA sequence with biological interpretation. They can guide studies of gene expression, evolution, genetic variation, and functional genomics. The same information also supports disease research by providing organized candidates and genomic features for examining how sequence differences may relate to biological processes.
By recording gene locations, structures, and potential functions, annotation provides a common framework for comparing genomes. Researchers can examine similarities and differences in annotated features to study evolution and species-specific characteristics. As new features are recognized in particular organisms, updated annotations make comparisons more accurate and help distinguish shared genomic patterns from lineage-specific ones.